Methods and apparatus to detect user attentiveness to handheld computing devices

ABSTRACT

Methods and apparatus to detect user attentiveness to handheld computing devices are disclosed. An example method includes detecting a change of a handheld computing device from a first starting spatial condition to a first ending spatial condition; and querying an index associating a plurality of spatial condition changes with respective likelihoods indicative of user attentiveness to determine if the index includes an entry having the first starting spatial condition and the first ending spatial condition of the detected change.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement and, moreparticularly, to methods and apparatus to detect user attentiveness tohandheld computing devices.

BACKGROUND

Audience measurement of media (e.g., content or advertisements)delivered in any format (e.g., via terrestrial, cable, or satellitetelevision and/or radio, stored audio and/or video played back from amemory such as a digital video recorder or an optical disc, a webpage,audio and/or video presented (e.g., streamed) via the Internet, videogames, etc.) often involves collection of media identifying data (e.g.,signature(s), fingerprint(s), code(s), tuned channel identificationinformation, time of exposure information, etc.) and people data (e.g.,user identifiers, demographic data associated with audience members,etc.). The media identifying data and the people data can be combined togenerate, for example, media exposure data indicative of amount(s)and/or type(s) of people that were exposed to specific piece(s) ofmedia.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an example handheld computing deviceincluding an example exposure measurement application constructed inaccordance with the teachings of this disclosure.

FIG. 2 is a block diagram of an example implementation of the exampleexposure measurement application of FIG. 1.

FIG. 3 is a block diagram of an example implementation of the exampleengagement detector of FIG. 2.

FIG. 4 is a flowchart representative of example machine readableinstructions that may be executed to implement the example exposuremeasurement application of FIGS. 1, 2 and/or 3.

FIG. 5 is a block diagram of an example processing platform capable ofexecuting the example machine readable instructions of FIG. 4 toimplement the example exposure measurement application of FIGS. 1, 2and/or 3.

DETAILED DESCRIPTION

In some audience measurement systems, exposure data is collected inconnection with usage of one or more computing devices. For example,audience measurement systems often employ one or more techniques todetermine user exposure to media via browsing the Internet via computingdevices. The exposure data can be correlated with the identities and/ordemographics of users to, for example, generate statistics for thedetected media. For example, an audience measurement entity (e.g.,Nielsen®) can calculate ratings and/or other statistics (e.g., onlineexposure statistics, such as a number of impressions for a web addressthat hosts an advertisement) for a piece of media (e.g., anadvertisement, a website, a movie, a song, an album, a news segment,personal video (e.g., a YouTube® video), a highlight reel, a televisionprogram, a radio program, etc.) accessed via a computing device bycrediting the piece of media as being presented on the computing deviceat a first time and identifying the audience member(s) using thecomputing device at the first time. Some known systems credit exposureto the media and generate statistics based on such creditingirrespective of the fact that the user(s) may be paying little or noattention to the presentation of the media.

Examples disclosed herein recognize that although media may be presentedon a computing device, a current user may or may not be paying attentionto (e.g., be engaged with) the presentation of the media. For example,when viewing online media (e.g., via a service such as Hulu®) on ahandheld computing device (e.g., an iPad® or iPhone®), users are oftenpresented with advertisements at one or more points or segments in thepresented programming. The user is typically unable to fast-forward orskip the advertisement. However, the user can easily disengage from(e.g., stop paying attention to) the handheld computing device duringpresentation of the advertisement by, for example, putting the handheldcomputing device down or turning the handheld computing device away fromview. In such instances, while the user did not actually pay attentionto the advertisement, a known prior monitoring service measuringexposure to the advertisement would credit the advertisement as beingwatched by the user even though the user did not watch theadvertisement.

Example methods, apparatus, and articles of manufacture disclosed hereinmeasure attentiveness of users of handheld computing devices withrespect to one or more pieces of media presented on the handheldcomputing devices. A first example measure of attentiveness for a userprovided by examples disclosed herein is referred to herein asengagement likelihood. As used herein, an engagement likelihoodassociated with a presented piece of media refers to a valuerepresentative of a confidence that the user is paying or has begunpaying attention to a presentation on a handheld computing device. Asecond example measure of attentiveness for a user provided by examplesdisclosed herein is referred to herein as disengagement likelihood. Asused herein, a disengagement likelihood associated with a presentedpiece of media refers to a value representative of a confidence that theuser is not paying (or has ceased paying) attention to a presentation ofthe handheld computing device.

As used herein, the term “handheld computing device” refers to any formof processor based device that can be, and is intended to besimultaneously held in the air and operated by one or more hands of auser. In other words, as used herein, a handheld computing device isreadily moved and held by the hand(s) of a user and is designed toreceive input from the user while being held in the air by the hand(s)of the user. While a handheld computing device can remain stationaryduring user operation, a handheld computing device is not one designedor mainly meant to remain stationary during interaction with a user,such as a desktop computer. For example, a handheld computing devicesuch as a tablet or smart phone can be placed on a table and operate bya user while resting on the table. However, unlike non-handheldcomputing devices such as desktop computers, the tablet can also bepicked up and operating by the user with one or both hands.

To determine a likelihood that a user is paying attention to (e.g.,engaged with) or not paying attention to (e.g., disengaged with) ahandheld computing device that is presenting media, examples disclosedherein utilize sensors of the handheld computing device (e.g.,gravitational sensors (e.g., accelerometers, gyroscopes, tilt sensors),microphones, magnetometers, global positioning sensors, etc.) to detectone or more spatial (e.g., position, movement and/or orientation)conditions related to the handheld computing device while, for example,the media is being presented. Example spatial conditions detected by thesensor(s) of the handheld computing device include an angularorientation or tilt relative to one o more reference lines (e.g., ahorizontal reference line, a vertical reference line, etc.), a distancefrom a nearest object (e.g., a user), a proximity to a person, etc.Examples disclosed herein also detect changes to current spatialconditions, such a change from a first orientation to a secondorientation and/or a change from a first position relative to a user toa second position relative to the user. Examples disclosed hereincompare detected change(s) to an index of likelihoods, each likelihoodcorresponding to a respective one of a plurality of possible changes(e.g., a first position to a second position). In other words, thelikelihoods of the index provided by examples disclosed herein areindicative of how likely a user is engaged or disengaged with apresentation on the handheld computing device when the user changes thehandheld computing device from a first spatial condition to a secondspatial condition. For example, a first example engagement likelihood ofthe example index disclosed herein indicates that the user is likely(e.g., according to a corresponding percentage) to be paying attentionto a screen of the handheld computing device and/or likely beginning topay attention to the screen of the handheld computing device when theuser changes the orientation of the handheld computing device fromparallel to the ground (e.g., resting on a table) to a forty-five degreeangle relative to a horizontal reference that is facing downward andparallel to the ground (e.g., being held above the user while the useris laying down). Conversely, a first example disengagement likelihood ofthe example index disclosed herein indicates that the user is unlikelyto be paying attention to the screen of the handheld computing deviceand/or likely to begin disengaging from the screen of the handheldcomputing device when the user changes the orientation of the handheldcomputing device from a forty-five degree angle relative to thehorizontal reference that is parallel to the ground to a position thatis parallel to the ground.

A second example disengagement likelihood of the example index disclosedherein indicates that the user is unlikely to be paying attention to thescreen of the handheld computing device and/or likely to begindisengaging from the screen of the handheld computing device when theuser changes a position of the handheld computing device relative to theuser from a first position proximate the user to a second position inwhich the user is undetected (e.g., the device is too far away from theuser for the sensors of the handheld computing device to determine adistance between the handheld computing device and the user).Conversely, a second example engagement likelihood of the example indexdisclosed herein indicates that the user is likely to be payingattention to the screen of the handheld computing device and/orbeginning to pay attention to the screen of the handheld computingdevice when the user changes a position of the handheld computing devicerelative to the user from the second position (e.g., an undetectabledistance from the user) to the first position (e.g., proximate theuser).

Other example engagement and disengagement likelihoods of the exampleindex disclosed herein correspond to changes in orientation combinedwith changes in relative position. In other words, some exampleengagement likelihoods of the example index disclosed herein indicatehow likely it is that the user is paying attention to or is beginning topay attention to the presentation on the handheld computing device whena certain change in orientation coincides with a certain change inrelative position (e.g., a change in distance between the device and theuser). Additionally or alternatively, some example disengagementlikelihoods of the example index disclosed herein indicate how likely itis that the user is not paying attention to or is beginning to disengagefrom the presentation on the handheld computing device when a certainchange in orientation coincides with a certain change in relativeposition (e.g., a change in distance between the device and the user).

Using the example index disclosed herein, user attentiveness to handheldcomputing devices can be passively collected. As a user interacts with ahandheld computing device, examples disclosed herein detect change(s) inorientation and/or relative position (e.g., of the device with respectto the user) and compare the detected change(s) to theengagement/disengagement likelihood index. If the detected change(s)correspond to (e.g., within a threshold) one or more of the changes ofthe engagement/disengagement likelihood index, examples disclosed hereindetermine that the corresponding likelihood represents how likely it isthat the current user is paying attention to the handheld computingdevice, beginning to pay attention to the handheld computing device, notpaying attention to the handheld computing device, and/or beginning todisengage from the handheld computing device. The attentivenessmeasurements provided by examples disclosed herein can be used to, forexample, increase granularity and accuracy of exposure measurement datagenerated in connection with the media being presented on the handheldcomputing device.

FIG. 1 is an illustration of an example household 100 including aplurality of household members 102, 104, and 106. The example household100 of FIG. 1 (e.g., a “Nielsen family”) has been statistically selectedby, for example, an audience measurement entity (e.g., The NielsenCompany®) for the purpose of developing statistics (e.g., ratings) for apopulation/demographic of interest. One or more persons of the household100 of the illustrated example have registered with the audiencemeasurement entity (e.g., by agreeing to be a panelist) and haveprovided their demographic information as part of the registration. Inthe illustrated example of FIG. 1, the provided demographic informationincludes identification information (e.g., user names, identifiers,etc.), age, gender, income level, etc. for each of the household members102-106. One or more of the household members 102-106 has access to ahandheld computing device 108 having a user interface 110. The examplehandheld computing device 108 of FIG. 1 is a tablet (e.g., an iPad®).However, the example handheld computing device 108 can be any other typeof handheld computing device, such as a smart phone (e.g., an iPhone®).

The example handheld device 108 of FIG. 1 includes an exposuremeasurement application 112 configured in accordance with teachings ofthis disclosure. As described in greater detail below in connection withFIGS. 2-4, the example exposure measurement application 112 calculatesinformation related to attentiveness of users of the handheld computingdevice 108 and detects media (e.g., an advertisement, a website, amovie, a song, an album, a news segment, personal video (e.g., aYouTube® video), a highlight reel, a television program, a radioprogram, etc.) presented on the handheld computing device 108. In theexample of FIG. 1, the exposure measurement application 112 communicatesattentiveness information and/or media identification information to adata collection facility 114 via a network 116 (e.g., a local-areanetwork, a wide-area network, a metropolitan-area network, the Internet,a digital subscriber line (DSL) network, a cable network, a power linenetwork, a wireless communication network, a wireless mobile phonenetwork, and/or a Wi-Fi network). In the illustrated example, the datacollection facility 114 is managed by an audience measurement entitythat provides the example exposure measurement application 112 to thehousehold 100. For example, the audience measurement entity associatedwith the data collection facility 114 makes the exposure measurementapplication 112 available for download onto the example handheldcomputing 108 over the network 116 and/or via any other suitablecommunication media (e.g., email, a disk, etc.). In some examples,several versions of the exposure measurement application 112 are madeavailable, each version being tailored to a specific operating systemand/or type or model of handheld computing device. Additionally, each ofthe versions of the exposure measurement application 112 may be madeavailable on a download service (e.g., Apple® App Store®) associatedwith the corresponding operating system and/or type or model of handheldcomputing device. Any suitable manner of installing the exposuremeasurement application 112 onto the example handheld computing device108 may be employed. While the example exposure measurement application112 is described herein in connection with the household 100 ofpanelists, the example exposure measurement application 112 disclosedherein can be installed and executed on handheld computing devicesassociated with individual panelists and/or handheld computing devicesassociated with non-panelists (e.g., the general public).

FIG. 2 is a block diagram of the example handheld computing device 108of FIG. 1 including an example implementation of the example exposuremeasurement application 112 of FIG. 1. The example handheld computingdevice 108 of FIG. 2 includes a plurality of sensors 200 a-e thatinclude one or more of gravitational sensors (e.g., accelerometers,gyroscopes, tilt sensors), a microphone, and/or global positioningsensors. The sensors 200 a-e collect data related to movements, tilts,orientations, paths of movement, etc. of the handheld computing device108. For example, one or more of the sensors 200 a-e may be athree-dimensional accelerometer capable of generating a chronologicalseries of vectors indicative of directional magnitudes of movementstaken by the example handheld device 108. In the illustrated example ofFIG. 2, data collected by the sensors 200 a-e is conveyed to a sensorinterface 202 of the example exposure measurement application 112. Theexample sensor interface 202 of FIG. 2 interprets, formats, and/orconditions the data provided by the sensors 200 a-e such that datacollected by the sensors 200 a-e is useable by the exposure measurementapplication 112. Thus, the example exposure measurement application 1122of FIG. 2 uses data provided by the sensors 200 a-e native to thehandheld computing device 108 and, thus, does not require installationor coupling of non-native sensors to the handheld computing device 108.That is, the example exposure measurement application 112 of theillustrated example utilizes existing sensors 200 a-e of the handheldcomputing device 108. In other examples, sensors are added to themonitored device.

To detect attentiveness of a current user of the handheld computingdevice 108 to a presentation of media on the handheld computing device108, the example exposure measurement application 112 includes anattentiveness detector 204. The example attentiveness detector 204 ofFIG. 2 receives sensor data from the sensor interface 202 related totilts or orientations, tilt or orientation changes, positions relativeto the user, changes in positions relative to the user, etc. experiencedby the handheld computing device 108 when, for example, the handheldcomputing device 108 is presenting media (e.g., while one or moreapplications of the handheld computing device 108 are outputting mediasuch as a movie, a song, an advertisement, etc.). As described in detailbelow in connection with FIGS. 3 and 4, the example attentivenessdetector 204 compares the received sensor data to anengagement/disengagement likelihood index to determine likelihood(s)that the user is engaged with the media, beginning to engage the media,disengaged from the media, and/or beginning to disengage from the media.

The example attentiveness detector 204 of FIG. 2 outputsengagement/disengagement likelihood information to a time stamper 206.The time stamper 206 of the illustrated example includes a clock and acalendar. The example time stamper 206 of FIG. 2 associates a time anddate with the engagement/disengagement information provided by theexample attentiveness detector 204 by, for example, appending thetime/date data to the end of the corresponding data. A data packageincluding, for example, the engagement/disengagement information, atimestamp, a type or identifier associated with the handheld computingdevice 108, registration information associated with the household 100and/or any of the members 102-106, etc. is stored in a memory 208. Whileshown as part of the example exposure measurement application 112 inFIG. 2, the memory 208 of the illustrated example is native to themonitored handheld computing device 108 and accessible to the exampleexposure measurement application 112. The memory 208 may include avolatile memory (e.g., Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM, etc.) and/or a non-volatile memory (e.g., flash memory). Thememory 210 may include one or more double data rate (DDR) memories, suchas DDR, DDR2, DDR3, mobile DDR (mDDR), etc. The memory 208 may alsoinclude one or more mass storage devices such as, for example, harddrive disk(s), solid state memory, etc.

The example exposure measurement application 112 of FIG. 2 also includesa media detector 210 and an output device 212. The example mediadetector 210 of FIG. 2 detects presentation(s) of media (e.g., a song, amovie, a website, a game, etc.) on the handheld computing device 108 andcollects media identifying information associated with the detectedpresentation(s). For example, the media detector 210 can identify apresentation time and a source of a presentation. The sourceidentification data may be, for example, a universal resource locator(URL) associated with a web address that hosts a movie, a televisionprogram presented via an online service (e.g., Hulu®), a song, etc. Theexample media detector 210 can obtain the URL by, for example,monitoring a browser of the handheld computing device 108 and/orselection(s) made on the user interface 110 of the handheld computingdevice 108. Additionally or alternatively, the media detector 210 mayutilize codes embedded and/or otherwise associated with media beingpresented on the handheld computing device 108 to identify thepresentation(s). As used herein, a code is an identifier that istransmitted with the media for the purpose of identifying and/or foraccessing the corresponding media. Codes may be carried in the audio, inthe video, in metadata, in a program guide, or in any other portion ofthe media and/or the signal carrying the media. Additionally oralternatively, the media detector 210 can collect a signaturerepresentative of a portion of the media. As used herein, a signature isa representation of some characteristic of the media (e.g., a frequencyspectrum of an audio signal). Signatures may be thought of asfingerprints of the media. Collected signature(s) can be comparedagainst a collection of signatures of known media to identify thecorresponding media. In some examples, the media detector 210 collectsthe signature(s). Additionally or alternatively, the media detector 210can collect samples of the media and export the samples to a remote sitefor generation of the signature(s). Irrespective of the manner in whichthe media of the presentation is identified (e.g., based on browsermonitoring, codes, metadata, and/or signatures), the mediaidentification information is time stamped by the time stamper 206 andmay be stored in the memory 208.

In some examples, the media detector 210 sends a signal to theattentiveness detector 204 in response to determining that the handheldcomputing device 108 is presenting media, thereby triggering theattentiveness detector 204 to collect user engagement/disengagementinformation. In such instances, the attentiveness detector 204 collectsand interprets data from the sensors 200 a-e while the handheldcomputing device 108 presents media such that the example attentivenessdetector 204 determines whether, for example, a user is paying attentionor beginning to pay attention to the handheld computing device 108 whenmedia is being presented on the handheld computing device 108. In otherwords, the example engagement detector 204 of FIG. 2 cooperates with themedia detector 210 to determine attentiveness of users to the handhelddevice 108 while media is being output.

In the illustrated example of FIG. 2, the output device 212 periodicallyand/or aperiodically exports the recorded data from the memory 208 tothe data collection facility 114 of FIG. 1 via the network 116. The datacollection facility 114 can analyze the data provided by the exampleexposure measurement application 112 in any suitable manner to, forexample, develop statistics regarding exposure of the identified usersand/or users having similar demographic(s) as the identified users.Alternatively, the data analysis could be performed locally and exportedvia the network 116 or the like to the data collection facility 114 forfurther processing. For example, user attentiveness information detectedin connection with the handheld computing device 108 (e.g., by theattentiveness detector 204) at a time (e.g., as indicated by the timestamp appended to the user attentiveness information (e.g., by the timestamper 206) at which media (e.g., an advertisement) is detected (e.g.,by the media detector 210) as presented on the handheld computing device108 can be used in a exposure rating calculation for the correspondingmedia (e.g., the advertisement).

While an example manner of implementing the exposure measurementapplication 112 of FIG. 1 has been illustrated in FIG. 2, one or more ofthe elements, processes and/or devices illustrated in FIG. 2 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example sensor interface 202, the exampleattentiveness detector 204, the example time stamper 206, the examplemedia detector 210, the example output device 212, and/or, moregenerally, the example exposure measurement application 112 of FIG. 2may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example sensor interface 202, the example attentivenessdetector 204, the example time stamper 206, the example media detector210, the example output device 212, and/or, more generally, the exampleexposure measurement application 112 of FIG. 2 could be implemented byone or more circuit(s), programmable processor(s), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)), etc. At least oneof the example sensor interface 202, the example attentiveness detector204, the example time stamper 206, the example media detector 210, theexample output device 212, and/or, more generally, the example exposuremeasurement application 112 of FIG. 2 are hereby expressly defined toinclude a tangible computer readable medium such as a memory, DVD, CD,Blu-ray, etc. storing the software and/or firmware. Further still, theexample exposure measurement application 112 of FIG. 2 may include oneor more elements, processes and/or devices in addition to, or insteadof, those illustrated in FIG. 2, and/or may include more than one of anyor all of the illustrated elements, processes and devices.

FIG. 3 is a block diagram of an example implementation of the exampleattentiveness detector 204 of FIG. 2. To determine an orientation of theexample handheld computing device 108 of FIGS. 1 and/or 2, the exampleattentiveness detector 204 of FIG. 3 includes an orientation detector300. In the illustrated example, the orientation detector 300 utilizesdata received from the sensor interface 202 of FIG. 2. For example, theorientation detector 300 uses data from the sensors 200 a-e thatinclude, for example, accelerometer(s), magnetometer(s), tilt sensor(s),etc. to determine an angle at which the handheld computing device 108 isorientated relative to a horizontal reference line (e.g., on which oneor more of the sensors 200 a-e are based). Such an angle is referred toherein as a horizontal orientation. Thus, when the handheld computingdevice 108 is resting on a table, the example orientation detector 300determines that the handheld computing device 108 is at a zero anglethat corresponds to the horizontal reference line. In contrast, when thehandheld computing device 108 is obtusely or acutely angled away fromthe horizontal reference line by an angular amount (e.g., forty-fivedegrees) while, for example, being held by a sitting user, the exampleorientation detector 300 determines that the handheld computing device108 is being held at the detected angle.

Additionally or alternatively, the orientation detector 300 uses datafrom the sensors 200 a-e to determine an angle at which the handheldcomputing device 108 is tilted or orientated relative to a secondreference line, such as a vertical reference line, of which the sensors200 a-e are aware. Such an angle is referred to herein as a verticalorientation. The example orientation detector 300 analyzes the verticalorientation of the handheld computing device 108 by determining whetherone side or edge of the handheld computing device 108 is higher than anopposing side or edge with reference to the vertical reference line.Thus, when the handheld computing device 108 is resting against a wallwith one edge on a flat surface, the example orientation detector 300determines that the handheld computing device 108 is at a zero tilt thatcorresponds to the vertical reference line. In contrast, when thehandheld computing device 108 is obtusely or acutely angled away/towardthe vertical reference line by an angular amount while, for example,being held by a user, the example orientation detector 300 determinesthat the handheld computing device 108 is being held at the detectedtilt (e.g., thirty degrees).

To determine a position relative to a user and/or other objects, theexample attentiveness detector 204 includes a position detector 302. Inthe illustrated example, the position detector 302 utilizes datareceived from the sensor interface 202 of FIG. 2. For example, theposition detector 302 uses data from the sensors 200 a-e that include,for example, proximity sensor(s), infrared sensor(s), temperaturesensor(s), microphone(s), speaker(s), etc. to determine a position ofthe handheld computing device 108 relative to, for example, a body of auser. For example, the position detector 302 may determine that ameasured temperature of a nearest object corresponds to a person orclothes being worn by a person. In such instances, the example positiondetector 302 measures a distance between the handheld computing device108 and the nearest object using, for example, a proximity sensor and/oran infrared sensor. Additionally or alternatively, the position detectormay determine that no object proximate the handheld computing device 108corresponds to a person and, thus, that no person is near the handheldcomputing device 108.

In the illustrated example, the orientation detector 300 and theposition detector 302 are triggered to collect and analyze data from thesensor interface 202 by, for example, the media detector 210 when themedia detector 210 determines that the handheld computing device 108 isoutputting media. Thus, in the illustrated example, the orientationdetector 300 detects orientation(s) of the handheld computing device 108when the handheld computing device 108 is presenting media and theposition detector 302 detects a position of the handheld computingdevice 108 relative to a user when the handheld computing device 108 ispresenting media to the user. In some examples, the example orientationdetector 300 and/or the position detector 302 records a type of mediabeing presented (e.g., as provided by the media detector 210 of FIG. 2)in association with the detected orientation(s) and/or relativeposition(s). Additionally or alternatively, the example orientationdetector 300 and/or the example position detector 302 can analyze sensordata from the sensor interface 202 when the handheld computing device108 is performing alternative operations and/or can continuously detectorientation(s) and/or relative position(s) regardless of an operatingstatus of the handheld computing device 108.

The example attentiveness detector 204 of FIG. 3 includes a changedetector 304 to detect changes (e.g., beyond a threshold magnitude) inorientation and/or relative position experienced by the handheldcomputing device 108. For example, the change detector 304 of FIG. 3determines that the handheld computing device 108 experienced a changein orientation when an angle at which the handheld computing device 108is orientated relative to a horizontal and/or vertical reference linechanges from a first angle to a second angle different from the angle bya threshold magnitude (e.g., a number of degrees, such as one degree,two degrees, etc.). Further, the example change detector 304 of FIG. 3determines that the handheld computing device 108 experienced a changein position relative to a user when a distance between the handheldcomputing device 108 and a user changes from a first distance to asecond distance different from the first distance by a thresholdmagnitude (e.g., a number of centimeters, a number of inches, such as 1centimeter, 1 inch, etc.).

When the example change detector 304 detects a change in orientationand/or position of the handheld computing device 108, the example changedetector 304 records a first set of spatial conditions (e.g., a firstorientation(s) and/or a first position relative to the user) associatedwith the handheld computing device 108 immediately prior to the detectedchange, as well as a second set of spatial conditions (e.g., secondorientation(s) and/or a second relative position) associated with thehandheld computing device 108 immediately after the detected change.Accordingly, with respect to a detected change in spatial condition(s)of the handheld computing device 108, the example change detector 304 ofFIG. 3 records a starting set of spatial conditions (e.g.,orientation(s) and/or a relative position) and an ending set of spatialconditions (e.g., orientation(s) and/or a relative position).

The example attentiveness detector 204 of FIG. 3 includes anengagement/disengagement likelihood index 306 that includes a pluralityof predefined spatial condition changes related to the handheldcomputing device 108. The predefined spatial condition changes of theexample likelihood index 306 of FIG. 3 may be set and updated by, forexample, administrators of programmers associated with the exampleexposure measurement application 112 of FIGS. 1 and/or 2 and/or theexample data collection facility 114 of FIG. 1. The predefined spatialcondition changes of the example likelihood index 306 of FIG. 3 include,for example, a first orientation change from a first startingorientation to a first ending orientation, a second orientation changefrom the first starting orientation to a second ending orientation, athird orientation change from the first starting orientation to a thirdending orientation, a fourth orientation change from a second startingorientation to the first ending orientation, a fifth orientation changefrom the second starting orientation to the second ending orientation, asixth orientation change from the second starting orientation to thethird ending orientation, etc. Further, the predefined spatial conditionchanges of the example likelihood index 306 of FIG. 3 include, forexample, a first position change from a first starting relative positionto a first ending relative position, a second position change from thefirst starting relative position to a second ending relative position, athird position change from the first starting relative position to athird ending relative position, a fourth position change from a secondstarting relative position to the first ending relative position, afifth position change from the second starting relative position to thesecond ending relative position, a sixth position change from the secondstarting relative position to the third ending relative position, etc.Further, the predefined spatial condition changes of the examplelikelihood index 306 include, for example, the first orientation changefrom above coinciding with the first position change from above, thefirst orientation change from above coinciding with the second positionchange from above, the second orientation change from above coincidingwith the third position change from above, etc.

Some of the predefined spatial condition changes of the example index306 are associated with an engagement likelihood, which reflects howlikely the respective change corresponds to a user being engaged with orbeginning to engage the handheld computing device 108. Additionally oralternatively, some of the predefined spatial condition changes of theexample index 306 are associated with a disengagement likelihood, whichreflects how likely the respective change corresponds to the user beingdisengaged or beginning to disengage from the handheld computing device108.

As described above, the example exposure measurement application 112 ofFIGS. 1 and/or 2 is made available to different types of handheldcomputing devices (e.g., tablets, smart phones, laptops, etc.), as wellas different specific brands or models of handheld computing devices.Accordingly, different versions of the example likelihood index 308 aremade available (e.g., via download from an online application store). Insome examples, the type or model of a handheld computing device 108 isautomatically detected (e.g., upon installation and/or download of theexposure measurement application 112) and a corresponding version of thelikelihood index 308 is installed and used by the example attentivenessdetector 204. The different versions of the likelihood index 308 aretailored to the corresponding types or models of the handheld devicesbecause different types or models of handheld devices are designed to behandled differently and/or have different characteristics that causeusers to handle the devices differently while interacting (e.g., playinga game, viewing media, etc.) with the devices. For example, a largerscreen size of a first type of handheld computing device compared to asecond handheld computing device may enable a user of the first type ofhandheld computing device to view the screen at a wider angle than thesecond type of handheld computing device. Additional or alternatively,some handheld computing devices are designed to receive different typesof motion related input (e.g., shaking, alteration of orientation tochange viewing mode, etc.) than others. As a result, certain motions,orientations, changes to relative position may correspond to a firstinteraction for a first type of handheld computing device and a secondinteraction for a second type of handheld computing device. Otherdifferences between handheld computing devices may be taken intoconsideration for the tailoring of the likelihood index 308 fordifferent devices. For example, the thresholds associated with thecorresponding likelihood index 308 for the particular types of handheldcomputing devices are customized for the particular characteristic(s)and/or user input configuration(s).

As described above, for each detected change in a spatial condition(e.g., an orientation or a relative position) above a threshold, theexample change detector 304 records a starting spatial condition and anending spatial condition. The example attentiveness detector 204 of FIG.3 includes a comparator 308 to compare the recorded starting and endingspatial conditions associated with detected changes to the entries ofthe engagement/disengagement likelihood index 306. In other words, theexample comparator 308 uses recorded spatial conditions associated witha detected change to query the likelihood index 306. Thus, the examplecomparator 308 determines whether the detected change corresponds to anyof the predefined spatial condition changes of the likelihood index 306.In the illustrated example, the comparator 308 determines whether thedetected change matches any of the predefined spatial condition changesof the example index 306 within a threshold or tolerance (e.g.,sufficiently similar). If so, the corresponding likelihood(s) of theindex 306 are applied to the detected change.

Because the detected spatial condition change may include more than oneaspect, the example comparator 308 may find more than one match in thelikelihood index 306. For example, suppose the change detector 304detects a change involving a first spatial condition change from a firsthorizontal orientation to a second horizontal orientation, as well as asecond spatial condition change from a first vertical horizontalorientation to a second horizontal orientation, as well as a thirdspatial change from a first relative position to a second relativeposition. In such an instance, the example comparator 308 may findmatches in the index 306 for the first and second spatial conditionchanges. Additionally or alternatively, the example comparator 308 mayfind a match in the index 306 for a combination or concurrence of thefirst and second spatial condition changes or match for a combination orconcurrence of the second and third spatial condition changes. As aresult, more than one likelihood from the index 306 may apply to thedetected change. For such instances, the example attentiveness detector204 includes an aggregator 310 to aggregate the plurality of likelihoodswhen a detected change involves more than one matching spatial conditionchange from the index 306. In the illustrated example of FIG. 3, theaggregator 310 averages the likelihoods. However, additional oralternative mathematical operations and/or algorithms (e.g.,calculations) may be employed by the example aggregator 310 of FIG. 3.

In some examples, the plurality of likelihoods are output individuallyas separate measurements of user attentiveness (e.g., without beingaggregated). For example, when a first one of the likelihoodscorresponds to an engagement likelihood and a second one of thelikelihoods corresponds to a disengagement likelihood, the comparator308 of such examples outputs the two likelihoods individually withoutaggregator the first and second likelihoods.

In the illustrated example, when a single match is found in thelikelihood index 306 for a detected change, the example comparator 308outputs the corresponding likelihood as representative of likelyengagement (or disengagement) of the current user with a presentation ofthe handheld computing device 108. Otherwise, in the illustratedexample, when more than one match is found in the likelihood index 306for a detected change, the example aggregator 310 outputs the aggregatedlikelihood as representative of likely engagement (or outputs theaggregated likelihood as representative of likely disengagement) of thecurrent user with a presentation of the handheld computing device 108.

The example attentiveness detector 204 of FIG. 3 also includes a useridentifier (ID) requestor 312 to request user identification informationfrom the current user in response to, for example, the change detector304 determining that that handheld computing device 108 is experiencingand/or experienced a spatial condition change and/or the comparator 308or aggregator outputting a likelihood of engagement or disengagementsuggesting a change in attentiveness. In the illustrated example, theuser ID requestor 310 generates a prompt on the user interface 110 thatrequests user identification information from the user such that theexposure measurement application 112 can attribute the detected userattentiveness to a particular one of, for example, the household members102-106.

While an example manner of implementing the attentiveness detector 204of FIG. 2 has been illustrated in FIG. 3, one or more of the elements,processes and/or devices illustrated in FIG. 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example orientation detector 300, the example positiondetector 302, the example change detector 304, the exampleengagement/disengagement likelihood index 306, the example comparator308, the example aggregator 310, the example user ID requester 312,and/or, more generally, the example attentiveness detector 204 of FIG. 3may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example orientation detector 300, the example positiondetector 302, the example change detector 304, the exampleengagement/disengagement likelihood index 306, the example comparator308, the example aggregator 310, the example user ID requester 312,and/or, more generally, the example attentiveness detector 204 of FIG. 3could be implemented by one or more circuit(s), programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)), field programmable gate array (FPGA), etc. At leastone of the example orientation detector 300, the example positiondetector 302, the example change detector 304, the exampleengagement/disengagement likelihood index 306, the example comparator308, the example aggregator 310, the example user ID requester 312,and/or, more generally, the example attentiveness detector 204 of FIG. 3are hereby expressly defined to include a tangible computer readablemedium such as a memory, DVD, CD, Bluray, etc. storing the softwareand/or firmware. Further still, the example attentiveness detector 204of FIG. 3 may include one or more elements, processes and/or devices inaddition to, or instead of, those illustrated in FIG. 3, and/or mayinclude more than one of any or all of the illustrated elements,processes and devices.

A flowchart representative of example machine readable instructions forimplementing the example exposure measurement application 112 of FIGS.1, 2 and/or 3 is shown in FIG. 4. In the example of FIG. 4, the machinereadable instructions comprise a program for execution by a processorsuch as the processor 512 shown in the example computer 500 discussedbelow in connection with FIG. 5. The program may be embodied in softwarestored on a tangible computer readable medium such as a CD-ROM, a floppydisk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or amemory associated with the processor 512, but the entire program and/orparts thereof could alternatively be executed by a device other than theprocessor 512 and/or embodied in firmware or dedicated hardware.Further, although the example programs are described with reference tothe flowchart illustrated in FIG. 4, many other methods of implementingthe example exposure measurement application 112 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined.

As mentioned above, the example processes of FIG. 4 may be implementedusing coded instructions (e.g., computer readable instructions) storedon a tangible computer readable storage medium such as a hard diskdrive, a flash memory, a read-only memory (ROM), a compact disk (CD), adigital versatile disk (DVD), a cache, a random-access memory (RAM)and/or any other storage media in which information is stored for anyduration (e.g., for extended time periods, permanently, brief instances,for temporarily buffering, and/or for caching of the information). Asused herein, the term tangible computer readable storage medium isexpressly defined to include any type of computer readable storage andto exclude propagating signals. Additionally or alternatively, theexample processes of FIG. 4 may be implemented using coded instructions(e.g., computer readable instructions) stored on a non-transitorycomputer readable storage medium such as a hard disk drive, a flashmemory, a read-only memory, a compact disk, a digital versatile disk, acache, a random-access memory and/or any other storage media in whichinformation is stored for any duration (e.g., for extended time periods,permanently, brief instances, for temporarily buffering, and/or forcaching of the information). As used herein, the term non-transitorycomputer readable storage medium is expressly defined to include anytype of computer readable medium and to exclude propagating signals. Asused herein, when the phrase “at least” is used as the transition termin a preamble of a claim, it is open-ended in the same manner as theterm “comprising” is open ended. Thus, a claim using “at least” as thetransition term in its preamble may include elements in addition tothose expressly recited in the claim.

FIG. 4 begins with an initiation of the example attentiveness detector204 of FIGS. 2 and/or 3 (block 400). In the example of FIG. 4, theattentiveness detector 204 is initiated when the example exposuremeasurement application 112 of FIGS. 1 and/or 2 is downloaded and/orinstalled on the handheld computing device 108 of FIGS. 1 and/or 2. Forexample, the first member 102 of the household 100 of FIG. 1 maydownload the exposure measurement application 112 via an onlineapplication service (e.g., iTunes®) as an application designed fortablets and/or smart phones. As described above, the installation of theexample exposure measurement application 112 onto the handheld computingdevice 108 sometimes includes determination of a type (e.g., tablet,smart phone, laptop, brand, model, etc.) of the handheld computingdevice 108) and installing the corresponding version of theengagement/disengagement likelihood index 306.

After installation, the exposure measurement application 112 runs in thebackground (e.g., does not require manual instantiation) and the examplesensor interface 202 of FIG. 2 conveys data to the example attentivenessdetector 204 including information related to one or more spatialconditions of the handheld computing device 108 (block 402). Based onthe information provided by the sensor interface 202, the exampleorientation detector 300 determines one or more orientations of thehandheld computing device 108 (block 404). For example, the orientationdetector 300 detects a horizontal orientation of the handheld computingdevice 108 and a vertical orientation of the handheld computing device108. Also based on the information provided by the sensor interface 202,the example position detector 302 determines a position of the handheldcomputing device 108 relative to, for example, a current user (block406). In some instances, the position detector 302 determines that thehandheld computing device 108 is at a certain distance away from theuser. Alternatively, in some instances, the example position detector302 determines that the handheld computing device 108 is not within adetectable distance of a user.

The example detector 304 determines whether the handheld computingdevice 108 has experienced one or more spatial condition changes fromthe orientation(s) and/or relative position determined at blocks 404 and406, respectively (block 408). When the example change detector 304detects such sufficient a change (e.g., a change greater than athreshold such as one percent), the change detector 304 instructs theorientation detector 300 and the position detector 302 to determine thenew spatial conditions (e.g., orientation(s) and/or relative position)of the handheld computing device 108. In response, the orientationdetector 300 uses data from the sensor interface 202 to determine theorientation(s) of the handheld computing device 108 (block 410).Further, the position detector 302 uses data from the sensor interface202 to determine the relative position of handheld computing device 108(block 412). The example change detector 304 records the orientation(s)and the relative position determined at blocks 404 and 406,respectively, as starting spatial conditions for the detected change(block 414). Further, the example change detector 304 records theorientation(s) and the relative position determined at blocks 410 and412, respectively, as the sending spatial conditions for the detectedchange (block 414).

The example comparator 308 uses the starting and ending spatialconditions associated with the detected change to query the exampleengagement/disengagement index 306 to determine whether the starting andending spatial conditions match any of the spatial condition changesstored in the index 306 (block 416). If a single match is found in theindex (block 418), the comparator 308 outputs the likelihood of theindex 306 corresponding to the match as a measure of attentiveness(e.g., engagement or disengagement) of a user of the handheld computingdevice 108 (block 420). Control then returns to block 402. Otherwise, ifmore than one match is found in the index 306 (block 422), theaggregator 310 aggregates (e.g., averages) the likelihoods of the index306 corresponding to the matches and outputs the aggregated likelihoodas a measure of attentiveness (e.g., engagement or disengagement) of auser of the handheld computing device 108 (block 424). Control returnsto block 402.

FIG. 5 is a block diagram of an example processor platform 500 capableof executing the instructions of FIG. 4 to implement the attentivenessdetector 204 of FIGS. 2 and/or 3. The processor platform 500 can be, forexample, a mobile phone (e.g., a cell phone), a personal digitalassistant (PDA), a tablet, a laptop computer, a handheld gaming device,or any other type of handheld computing device.

The processor platform 500 of the instant example includes a processor512. For example, the processor 512 can be implemented by one or moremicroprocessors or controllers from any desired family or manufacturer.

The processor 512 is in communication with a main memory including avolatile memory 514 and a non-volatile memory 516 via a bus 518. Thevolatile memory 514 may be implemented by Synchronous Dynamic RandomAccess Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUSDynamic Random Access Memory (RDRAM) and/or any other type of randomaccess memory device. The non-volatile memory 516 may be implemented byflash memory and/or any other desired type of memory device. Access tothe main memory 514, 516 is controlled by a memory controller.

The processor platform 500 also includes an interface circuit 520. Theinterface circuit 520 may be implemented by any type of interfacestandard, such as an Ethernet interface, a universal serial bus (USB),and/or a PCI express interface.

One or more input devices 522 can be connected to the interface circuit520. The input device(s) 522 permit a user to enter data and commandsinto the processor 512. The input device(s) can be implemented by, forexample, a keyboard, a mouse, a touchscreen, a track-pad, a trackball,isopoint and/or a voice recognition system.

One or more output devices 524 can be connected to the interface circuit520. The output devices 524 can be implemented, for example, by displaydevices (e.g., a liquid crystal display, a touchscreen, and/orspeakers). The interface circuit 520, thus, typically includes agraphics driver card.

The interface circuit 520 also includes a communication device such asan antenna, a modem or network interface card to facilitate exchange ofdata with external computers via a network 526 (e.g., a WiFi network, anEthernet connection, a digital subscriber line (DSL), a telephone line,coaxial cable, a cellular system, etc.).

The processor platform 500 also includes one or more mass storagedevices 528, such as a hard drive for storing software and data. Themass storage device 528 may implement the memory 208 of FIG. 2.

The coded instructions 532 of FIG. 4 may be stored in the mass storagedevice 528, in the volatile memory 514, and/or in the non-volatilememory 516.

Although certain example apparatus, methods, and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all apparatus,methods, and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method, comprising: detecting a change of ahandheld computing device from a first starting spatial condition to afirst ending spatial condition, wherein the first starting spatialcondition and the first ending spatial condition are angularorientations of the handheld computing device relative to a reference;and querying, via a logic circuit, an index associating a plurality ofspatial condition changes with respective likelihoods indicative of userattentiveness to determine if the index includes an entry having thefirst starting spatial condition and the first ending spatial conditionof the detected change, wherein a first plurality of the spatialcondition changes of the index are associated with correspondingengagement likelihoods representing how likely a respective one of thefirst plurality of spatial condition changes corresponds to a userbeginning to pay attention to a presentation on the handheld computingdevice.
 2. A method as defined in claim 1, wherein a second plurality ofthe spatial condition changes of the index are associated withrespective disengagement likelihoods representing how likely arespective one of the second plurality of spatial condition changescorresponds to the user beginning to disengage from the presentation onthe handheld computing device.
 3. A method as defined in claim 2,wherein a third plurality of the spatial condition changes of the indexare associated with respective engagement likelihoods representing howlikely a respective one of the first plurality of spatial conditionchanges coinciding with a respective one of the second plurality ofspatial condition changes corresponds to the user beginning to engagewith the presentation on the handheld computing device.
 4. A method asdefined in claim 2, wherein a third plurality of the spatial conditionchanges of the index are associated with respective disengagementlikelihoods representing how likely a respective one of the firstplurality of spatial condition changes coinciding with a respective oneof the second plurality of spatial condition changes corresponds to theuser beginning to disengage from the presentation on the handheldcomputing device.
 5. A method as defined in claim 1, further comprising,when the index includes a first entry having the first starting spatialcondition and the first ending spatial condition of the detected change,selecting a likelihood associated with the first entry as a measure ofuser attentiveness to the handheld computing device.
 6. A method asdefined in claim 1, wherein the detected change includes the handheldcomputing device changing from a second starting spatial condition to asecond ending spatial condition, and further comprising querying theindex to determine if the index includes an entry having the secondstarting spatial condition and the second ending spatial condition ofthe detected change.
 7. A method as defined in claim 6, furthercomprising, when the index includes a first entry having the firststarting spatial condition and the first ending spatial condition of thedetected change and a second entry having the second starting spatialcondition and the second ending spatial condition of the detectedchange, aggregating a first likelihood associated with the first entrywith a second likelihood associated with the second entry to form ameasure of user attentiveness to the handheld computing device.
 8. Atangible machine readable storage medium comprising instructions storedthereon that, when executed, cause a machine to at least: detect achange of a handheld computing device from a first starting spatialcondition to a first ending spatial condition, wherein the firststarting spatial condition and the first ending spatial condition areangular orientations of the handheld computing device relative to areference; and query an index associating a plurality of spatialcondition changes with respective likelihoods indicative of userattentiveness to determine if the index includes an entry having thefirst starting spatial condition and the first ending spatial conditionof the detected change, wherein a first plurality of the spatialcondition changes of the index are associated with correspondingengagement likelihoods representing how likely a respective one of thefirst plurality of spatial condition changes corresponds to a userbeginning to pay attention to a presentation on the handheld computingdevice.
 9. A machine readable storage medium method as defined in claim8, wherein a second plurality of the spatial condition changes of theindex are associated with corresponding disengagement likelihoodsrepresenting how likely a respective one of the second plurality ofspatial condition changes corresponds to the user beginning to disengagefrom the presentation on the handheld computing device.
 10. A machinereadable storage medium method as defined in claim 9, wherein a thirdplurality of the spatial condition changes of the index are associatedwith corresponding engagement likelihoods representing how likely arespective one of the first plurality of spatial condition changescoinciding with a respective one of the second plurality of spatialcondition changes corresponds to the user beginning to engage with thepresentation on the handheld computing device.
 11. A machine readablestorage medium method as defined in claim 9, wherein a third pluralityof the spatial condition changes of the index are associated withcorresponding disengagement likelihoods representing how likely arespective one of the first plurality of spatial condition changescoinciding with a respective one of the second plurality of spatialcondition changes corresponds to the user beginning to disengage fromthe presentation on the handheld computing device.
 12. A machinereadable storage medium method as defined in claim 8, wherein theinstructions, when executed, cause the machine to, when the indexincludes a first entry having the first starting spatial condition andthe first ending spatial condition of the detected change, select alikelihood associated with the first entry as a measure of userattentiveness to the handheld computing device.
 13. A machine readablestorage medium method as defined in claim 8, wherein the detected changeincludes the handheld computing device changing from a second startingspatial condition to a second ending spatial condition, and wherein theinstructions, when executed, cause the machine to query the index todetermine if the index includes an entry having the second startingspatial condition and the second ending spatial condition of thedetected change.
 14. A machine readable storage medium method as definedin claim 13, wherein the instructions, when executed, cause the machineto, when the index includes a first entry having the first startingspatial condition and the first ending spatial condition of the detectedchange and a second entry having the second starting spatial conditionand the second ending spatial condition of the detected change,aggregate a first likelihood associated with the first entry with asecond likelihood associated with the second entry to form a measure ofuser attentiveness to the handheld computing device.
 15. An apparatus,comprising: a detector to identify a change of a handheld computingdevice from a first starting spatial condition to a first ending spatialcondition based on a signal generated by a sensor of the handheldcomputing device, wherein the detector comprises a position detector tocalculate a position of the handheld computing device relative to auser; an index having a plurality of spatial condition changesassociated with likelihoods indicative of user attentiveness, wherein afirst plurality of the spatial condition changes of the index areassociated with corresponding engagement likelihoods representing howlikely a respective one of the first plurality of spatial conditionchanges corresponds to the user beginning to pay attention to apresentation on the handheld computing device; and a comparator todetermine whether the index includes an entry having the first startingspatial condition and the first ending spatial condition of theidentified change.
 16. An apparatus as defined in claim 15, wherein thedetector comprises an orientation detector to calculate an angularposition of the handheld computing device.
 17. An apparatus as definedin claim 15, wherein a second plurality of the spatial condition changesof the index are associated with corresponding disengagement likelihoodsrepresenting how likely a respective one of the second plurality ofspatial condition changes corresponds to the user beginning to disengagefrom the presentation on the handheld computing device.
 18. An apparatusas defined in claim 17, wherein a third plurality of the spatialcondition changes of the index are associated with correspondingengagement likelihoods representing how likely a respective one of thefirst plurality of spatial condition changes coinciding with arespective one of the second plurality of spatial condition changescorresponds to the user beginning to engage with the presentation on thehandheld computing device.
 19. An apparatus as defined in claim 17,wherein a third plurality of the spatial condition changes of the indexare associated with corresponding disengagement likelihoods representinghow likely a respective one of the first plurality of spatial conditionchanges coinciding with a respective one of the second plurality ofspatial condition changes corresponds to the user beginning to disengagefrom the presentation on the handheld computing device.
 20. An apparatusas defined in claim 15, wherein the comparator is to, when the indexincludes a first entry having the first starting spatial condition andthe first ending spatial condition of the detected change, select alikelihood associated with the first entry as a measure of userattentiveness to the handheld computing device.
 21. An apparatus asdefined in claim 15, further comprising an aggregator to, when the indexincludes a first entry having the first starting spatial condition andthe first ending spatial condition of the detected change and a secondentry having a second detected starting spatial condition and a seconddetected ending spatial condition of a second identified change, combinea first likelihood associated with the first entry with a secondlikelihood associated with the second entry to form a measure of userattentiveness to the handheld computing device.
 22. A method,comprising: detecting a change of a handheld computing device from afirst starting spatial condition to a first ending spatial condition,wherein the first starting spatial condition and the first endingspatial condition are distances between the handheld computing deviceand a user of the handheld computing device; and querying, via a logiccircuit, an index associating a plurality of spatial condition changeswith respective likelihoods indicative of user attentiveness todetermine if the index includes an entry having the first startingspatial condition and the first ending spatial condition of the detectedchange, wherein a first plurality of the spatial condition changes ofthe index are associated with corresponding engagement likelihoodsrepresenting how likely a respective one of the first plurality ofspatial condition changes corresponds to the user beginning to payattention to a presentation on the handheld computing device.
 23. Atangible machine readable storage medium comprising instructions storedthereon that, when executed, cause a machine to at least: detect achange of a handheld computing device from a first starting spatialcondition to a first ending spatial condition, wherein the firststarting spatial condition and the first ending spatial condition aredistances between the handheld computing device and a user of thehandheld computing device; and query an index associating a plurality ofspatial condition changes with respective likelihoods indicative of userattentiveness to determine if the index includes an entry having thefirst starting spatial condition and the first ending spatial conditionof the detected change, wherein a first plurality of the spatialcondition changes of the index are associated with correspondingengagement likelihoods representing how likely a respective one of thefirst plurality of spatial condition changes corresponds to a userbeginning to pay attention to a presentation on the handheld computingdevice.
 24. An apparatus, comprising: a detector to identify a change ofa handheld computing device from a first starting spatial condition to afirst ending spatial condition based on a signal generated by a sensorof the handheld computing device, wherein the detector comprises anorientation detector to calculate an angular position of the handheldcomputing device; an index having a plurality of spatial conditionchanges associated with likelihoods indicative of user attentiveness,wherein a first plurality of the spatial condition changes of the indexare associated with corresponding engagement likelihoods representinghow likely a respective one of the first plurality of spatial conditionchanges corresponds to the user beginning to pay attention to apresentation on the handheld computing device; and a comparator todetermine whether the index includes an entry having the first startingspatial condition and the first ending spatial condition of theidentified change.